本網訊（通訊員：郭佳）近日，williamhill威廉希尔官网大四學生肖海洋以第一作者身份在中科院三區期刊IEEE Access 上發表了了題為 “A Twinning Memory Bare-Bones Particle Swarm Optimization Algorithm for No-Linear Functions“的論文。該論文聚焦于複雜單目标優化問題，提出了一種新的粒子群優化策略，對傳統粒子群算法的效率提升具有重要意義，該論文指導老師為我院人工智能PI團隊青年教師郭佳。
Abstract
Been trapped by local minimums is an important problem in no-linear optimization problems, which is blocking evolutionary algorithms to find the global optimum. Normally, to increase the optimization accuracy,evolutionary algorithms implement search around the best individual. However, overuse of information from a single individual can lead to a rapid diversity losing of the population, and thus reduce the search ability. To overcome this problem, a twinning memory bare-bones particle swarm optimization (TMBPSO) algorithm is presented in this work. The TMBPSO contains a twining memory storage mechanism (TMSM) and a multiple memory retrieval strategy (MMRS). The TMSM enables an extra storage space to extend the search ability of the particle swarm and the MMRS enhances the local minimum escaping ability of the particle swarm. The particle swarm is endowed with the ability of self-rectification by the cooperation of the TMSM and the MMRS. To verify the search ability of the TMBPSO, the CEC2017 benchmark functions and five state-of-the-art population-based optimization algorithms are selected in experiments. Finally, experimental results confirmed that the TMBPSO can obtain high accurate results for no-linear functions.
論文内容詳細内容鍊接：https://ieeexplore.ieee.org/document/9953089